A simplified model of energy performance indicators for sustainable energy management

Three energy benchmarks, i.e., Specific Energy Consumption (SEC), Energy Utilization Index (EUI) and Energy Performance Indicator (EnPI), were investigated for assessment of energy consumption in twenty-three designated public university buildings in Thailand. Advantages and limitations of these benchmarks were reviewed. Among the three energy benchmarks, the SEC is more convenient to be used since all required data are readily available for use. However, the SEC must be used for its own sector. Actual energy consumption of these buildings was compared with the EUI baseline energy consumption. The EUI equation was developed based on the 2007 energy consumption data. The EnPI used the up-to-date data from the building energy auditing. It is imperative that several parameters must be sufficiently collected to establish the EnPI baseline energy consumption. To establish energy benchmarks properly, policymakers need to understand which benchmark is suitable as an indicator for energy consumption for each building.

Energy efficiency (EE) improvement is one of the most crucial elements in the decarbonization of industry. EE potential within industries largely remains untapped due to the lack of information regarding potential EE measures (EEM), knowledge regarding energy use, and due to the existence of some inconsistencies in the evaluation of energy use. Classification of energy end-using processes would increase the understanding of energy use, which in turn would increase the detection and deployment of EEMs. The study presents a novel taxonomy with hierarchical levels for energy end-use in manufacturing operations for the engineering industry, analyzes processes in terms of energy end-use (EEU) and CO2 emissions, and scrutinizes energy performance indicators (EnPIs), as well as proposing potential new EnPIs that are suitable for the engineering industry. Even though the study has been conducted with a focus on the Swedish engineering industry, the study may be generalizable to the engineering industry beyond Sweden.

The growing attention towards environmental sustainability in the pharmaceutical industry and increased awareness of the potential for improving energy performance are justified by the fact that the sector is energy intensive. However, the variety of the processes and the lack of data about production and energy consumption make it difficult to calculate Energy Performance Indicators (EnPIs) as much as to list Energy Performance Improvements Actions (EPIAs). This work elaborates data, such as final energy consumption and site characteristics, from 84 mandatory Italian Energy Audits (EAs) to calculate the mean and standard deviation of site-level EnPIs. Additionally, the suggested and implemented EPIAs are analyzed to describe achieved and potential savings. The results show what follows. In the typical pharmaceutical plant, around 70% of energy is used in auxiliary services, and its use is not related to production. For this reason, EnPIs calculated both with respect to mass production and plant surfaces have a mid-to-wide standard deviation; the mean primary energy EnPI calculated with respect to plant surface area is 0.38 ± 0.22 toe/m2. Most suggested EPIAs regard cold and hot energy production, as well as on-site energy production, from renewables and Combined Heat and Power (CHP) plants. The payback time is less than 4 years for many EPIAs, including both technical and managerial ones. According to the results, plant energy managers should calculate site EnPIs with respect to the site surface and increase monitoring of energy consumption at the process level. The last recommendation is also likely to be associated with more effective planning of EPIAs, allowing their introduction where the saving potential and economic indicators are more promising.

Energy performance index of air distribution: Thermal utilization effectiveness

From energy targets setting to energy-aware operations control and back: An advanced methodology for energy efficient manufacturing

The energy performance of an existing building is the amount of energy consumed to meet various needs associated with the standardized use of a building and is reflected in one or more indicators known as Building Energy Performance Indicators (EnPIs). These indicators are distributed amongst six main factors influencing energy consumption: climate, building envelope, building services and energy systems, building operation and maintenance, occupants' activities and behaviour, and indoor environmental quality. Any improvement made to either the existing structure or the physical and operational upgrade of a building system that enhances energy performance is considered an energy efficiency retrofit. The main goal of this research is to support the implementation of multifamily residential building energy retrofits through expert knowledge consensus on EnPIs for energy efficiency retrofit planning. The research methodology consists of a comprehensive literature review which has identified 35 EnPIs for assessing performance of existing residential buildings, followed by a ranking questionnaire survey of experts in the built-environment to arrive at a priority listing of indicators based on mean rank. This was followed by concordance analysis and measure of standard deviation. A total of 280 experts were contacted globally for the survey, and 106 completed responses were received resulting in a 37.85% response rate. The respondents were divided into two groups for analysis: academician/researchers and industry practitioners. The primary outcome of the research is a priority listing of EnPIs based on the quantitative data from the knowledge-base of experts from these two groups. It is the outcome of their perceptions of retrofitting factors and corresponding indicators. A retrofit strategy consists of five phases for retrofitting planning in which the second phase comprises an energy audit and performance assessment and diagnostics. This research substantiates the performance assessment process through the identification of EnPIs.

Organizations that implement strategic energy management programs have the potential to achieve sustained energy savings if the programs are carried out properly. A key opportunity for achieving energy savings that plant managers can take is to determine an appropriate level of energy performance by comparing their plant's performance with that of similar plants in the same industry. Manufacturing facilities can set energy efficiency targets by using performance-based indicators. The U.S. Environmental Protection Agency (EPA), through its ENERGY STAR{reg_sign} program, has been developing plant energy performance indicators (EPIs) to encourage a variety of U.S. industries to use energy more efficiently. This report describes work with the corn refining industry to provide a plant-level indicator of energy efficiency for facilities that produce a variety of products--including corn starch, corn oil, animal feed, corn sweeteners, and ethanol--for the paper, food, beverage, and other industries in the United States. Consideration is given to the role that performance-based indicators play in motivating change; the steps needed to develop indicators, including interacting with an industry to secure adequate data for an indicator; and the actual application and use of an indicator when complete. How indicators are employed in the EPA's efforts to encourage industries to voluntarily improve their use of energy is discussed as well. The report describes the data and statistical methods used to construct the EPI for corn refining plants. Individual equations are presented, as are the instructions for using them in an associated Excel spreadsheet.

Hospitals or health care buildings are social facilities designed to operate in general over 24 hours/day. Improving the energy efficiency of these facilities is an economic, environmental and societal challenge. This work aims at identifying the critical energy performance indicators in hospitals which could be monitored and optimized for an efficient energy management. This paper suggest an optimal approach for good practices of energy consumption in hospitals, especially, in Morocco. For energy optimization, it is wise to start by quantification and monitoring of energy performances indicators including the energy consumption of the most significant energy uses. The old health buildings were designed mainly according to criteria related to a care quality and service but the energy performance criteria was rarely studied. Hence, this strategy often leads a conception of infrastructure with significant and un-optimized energy consumption. Therefore, the energy consumptions were not the object of particular attention and the needs, no longer mastered, resulting in significant costs. The objective is to define the relevant factors, which significantly impact the energy performance. In this paper a methodology on understanding energy uses and evaluation of energy performance levels is developed for achievement of desired economic performance while preserving or even improve the comfort of patients and medical staff, the continuity of service and the of hygiene and safety level.

Actions aiming to reduce energy consumption directly contribute to the reduction of manufacturing costs and carbon footprint while supporting manufacturing processes’ productivity. Resistance spot welding is relevant in the automotive sector. Due to its operational characteristics, this process has high energy consumption. Despite this fact, few studies have found to guide solutions for its reduction. In this sense, this study proposes a method to improve the resistance spot welding process’s energy performance without compromising its quality. This study applies statistical analysis (ANOVA) to support predictive models that characterise energy and quality performance. The statistical analyses confirmed and quantified the influence of the control factors in energy and quality performance indicators. The predictive models made it possible to anticipate energy consumption and quality behaviour from adjustments in the welding line process parameters studied in this paper. To fit the best compromise between energy consumption and quality, energy labels to classify the process’s energy performance were proposed. The best compromise solution for the studied process parameter ranges in this work was as follows: $${C}_{wel}$$ = 8 kA, $${T}_{wel}$$ = 8 cycles, and $${F}_{ele}$$ = 3.3 kN. This parameter combination results in a consumption of approximately 2 Wh per spot weld. Approximately 33% less than the average estimated consumption per spot weld in the automotive industry.

The growth of volume of tunneling, power supplied per job, and consumption of fuel and energy resources makes it necessary to increase energy performance of production processes with reducing energy losses. Tunneling conditions are determined by a combination of mutually influencing factors (geological, technological and organizational), and assessing their impact on tunneling energy performance requires a deep detailed study. For criterion assessment of tunneling performance, indicators of energy consumption, performance, and quality of tunneling performed by shift crews, allowing to objectively assess their work, were proposed. Indicators of process and specific power consumption in the process of tunneling vary over a wide range, therefore, to ensure smooth equipment operation, shift crews must adhere to the recommended indicators that determine the optimum rates of tunneling and enables adherence to permissible operation modes. Statistical models of energy performance indicators of heading sets of equipment operation were investigated using the example of the Severnaya coal mine. Indicators of energy consumption, energy performance, and tunneling (on shift basis) were proposed. Distribution laws have been determined for the main indicators characterizing tunneling energy performance. Recommendations have been developed to ensure sustainable operation of heading sets of equipment throughout the entire period of tunneling. Tunneling requires permanent monitoring its parameters and rates of advance, the quality of face preparation, timely maintenance and repair of machinery and equipment, control of the process through ensuring optimal operating modes of the heading sets of equipment.

In Florida, an Energy Performance Index (EPI) calculation must be performed and submitted before a building permit can be granted. The EPI is a measure of energy efficiency calculated for all new construction and renovations. It uses data on several components of a structure to assign a rating (it must be under 100 points to pass). The lower the EPI, the more efficient the structure should be. This study involved the collection of residential EPI data from participating Gainesville Regional Utilities customers, matching this data with the actual energy consumption data and training an artificial neural network to relate the EPI to actual energy consumption. The ability of the artificial neural network to predict annual energy consumption will help residential designers/builders advance the goals of reducing the monthly cost of new housing and reducing the associated environmental impact and energy use. Data on new residential construction EPI calculations for 1998-2000 in Alachua County and corresponding energy consumption data for one year was compiled. This data was also matched with the conditioned living area of each house and then imported into the neural network. A local subdivision in which several houses were issued permits based on the same EPI was also identified as a control group so that normal variations in annual energy consumption could be determined. The neural network was used to create a model to predict annual energy consumption cost.

In this study, the energy consumption of three government and three private office buildings in Lucknow was investigated, and the energy performance index (EPI) for each building was determined. The main purpose of this research was to assess the energy usage of the buildings and identify factors affecting the energy usage. An analysis was performed using data from an energy audit of government buildings, electricity bills of private office buildings, and an on-site visit to determine building envelope materials and its systems. The annual energy consumption of buildings has been evaluated through EPI. The EPI, measured in kilowatt hour per square meter per year, is annual energy consumption in kilowatt hours divided by the gross floor area of the building in square meters. In this study, the energy benchmark for day-time-use office buildings in composite climate specified by Energy Conservation Building Code (ECBC) has been compared with the energy consumption of the selected buildings. Consequently, it has been found that the average EPI of the selected buildings was close to the national energy benchmark indicated by ECBC. Moreover, factors causing inefficient energy consumption were determined, and solutions for consistent energy savings are suggested for buildings in composite climate.

An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks

Proposing energy performance indicators to identify energy-wasting operations on big time-series data

Global reliance on fossil fuels poses significant challenges, including resource scarcity, price volatility, and the escalating climate crisis. The residential sector, accounting for a substantial portion of global energy consumption (30%) and CO2 emissions (28%), holds immense, yet often untapped, potential for energy efficiency. The study of Energy Performance Indicators (EnPIs) is therefore crucial. EnPIs serve as vital benchmarking tools to effectively measure, compare, and evaluate the energy performance of residential properties, thereby facilitating informed investment decisions for efficiency enhancements. This study aims to establish the relationship between installed power capacity (kVA) and electricity consumption per Gross Floor Area (GFA) to derive relevant EnPI values as a baseline. A quantitative approach utilizing a cross-sectional survey was employed. Data on installed power and monthly kilowatt-hour (kWh) consumption were collected from a diverse sample of residential units, encompassing common power variations (1300 VA, 2200 VA, 3300 VA, and 6600 VA). The collected data underwent analysis using descriptive statistics and regression analysis to identify key patterns and correlations. The findings indicate a clear trend: the EnPI (kWh/m²/year) increases with higher installed power variations. Specifically, the average EnPI values were found to be 50 for 1300 VA, 80 for 2200 VA, 97 for 3300 VA, and 118 for 6600 VA. A strong positive correlation was observed between power variation and EnPI (R² = 0.851), suggesting that 85.1% of the variation in EnPI is attributable to the installed power capacity. Furthermore, the analysis revealed that 91.19% of monthly kWh consumption is influenced by kVA. Based on these findings, specific kWh-month/kVA ratios were derived (e.g., 321 for 1.3 kVA; 455 for 2.2 kVA; 490 for 3.3 kVA; and 521 for 6.6 kVA). These two sets of developed EnPIs—kWh/m²/year and the kWh-month/kVA ratio—are considered crucial and can serve as valuable references for developing effective energy efficiency and conservation strategies within Indonesia's residential sector.